If I ask you, “Where are you now?’” or “What do your surroundings look like?” you will immediately be able to answer owing to a unique ability known as multisensory perception in humans that allows you to perceive your motion and your surrounding environment ensuring you have complete spatial awareness. But think as if the same question is posed to a robot: how would it approach the challenge?
The issue is if this robot does not have a map, it cannot know where it is, and if it does not know what its surroundings look like, neither can it create a map. Essentially, making this a ‘who came first, chicken or egg?’ problem which in the machine learning world in this context is termed as a localization and mapping problem.
“Localization” is the capability to acquire internal system information related to a robot’s motion, including its position, orientation, and speed. On the other hand, “mapping” pertains to the ability to perceive external environmental conditions, encompassing aspects such as the shape of the surroundings, their visual characteristics, and semantic attributes. These functions can operate independently, with one focused on internal states and the other on external conditions, or they can work together as a single system known as Simultaneous Localization and Mapping (SLAM).
The existing challenges with algorithms such as image-based relocalization, visual odometry, and SLAM include imperfect sensor measurements, dynamic scenes, adverse lighting conditions, and real-world constraints that somewhat hinder their practical implementation. The image above demonstrates how individual modules can be integrated into a deep learning-based SLAM system. This piece of research presents a comprehensive survey on how deep learning-based approaches and traditional approaches and simultaneously answers two essential questions:
Is deep learning promising for visual localization and mapping?
Researchers believe three properties listed below could make deep learning a unique direction for a general-purpose SLAM system in the future.
First, deep learning offers powerful perception tools that can be integrated into the visual SLAM front end to extract features in challenging areas for odometry estimation or relocalization and provide dense depth for mapping.
Second, deep learning empowers robots with advanced comprehension and interaction capabilities. Neural networks excel at bridging abstract concepts with human-understandable terms, like labeling scene semantics within a mapping or SLAM systems, which are typically challenging to describe using formal mathematical methods.
Finally, learning methods allow SLAM systems or individual localization/mapping algorithms to learn from experience and actively exploit new information for self-learning.
How can deep learning be applied to solve the problem of visual localization and mapping?
Deep learning is a versatile tool for modeling various aspects of SLAM and individual localization/mapping algorithms. For instance, it can be employed to create end-to-end neural network models that directly estimate pose from images. It is particularly beneficial in handling challenging conditions like featureless areas, dynamic lighting, and motion blur, where conventional modeling methods may struggle.
Deep learning is used to solve association problems in SLAM. It aids in relocalization, semantic mapping, and loop-closure detection by connecting images to maps, labeling pixels semantically, and recognizing relevant scenes from previous visits.
Deep learning is leveraged to discover features relevant to the task of interest automatically. By exploiting prior knowledge, e.g., the geometry constraints, a self-learning framework can automatically be set up for SLAM to update parameters based on input images.
It may be pointed out that deep learning techniques rely on large, accurately labeled datasets to extract meaningful patterns but may have difficulty generalizing to unfamiliar environments. These models lack interpretability, often functioning as black boxes. Additionally, localization and mapping systems can be computationally intensive while highly parallelizable unless model compression techniques are applied.
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